This repository provides a collection of Python notebooks and code snippets designed to help developers integrate and leverage Anthropic's Claude AI models. It targets developers looking to build applications with Claude, offering practical examples for various use cases like classification, retrieval augmented generation, summarization, and tool integration.

How It Works

The cookbook offers a practical, code-first approach to using Claude. It demonstrates specific techniques through runnable Jupyter notebooks, covering areas such as enhancing Claude's knowledge with external data (Retrieval Augmented Generation), enabling Claude to interact with external tools and APIs (Tool Use), and processing image inputs (Vision). The examples are designed for easy adaptation and integration into custom projects.

Quick Start & Requirements

• Install: Primarily uses Python notebooks. Clone the repository and install dependencies via pip install -r requirements.txt.
• Prerequisites: An Anthropic API key is required. Python 3.x is assumed.
• Resources: No specific hardware requirements beyond standard development environments.
• Links:
  • Anthropic developer documentation: https://docs.anthropic.com/
  • Anthropic support docs: https://support.anthropic.com/
  • Anthropic Discord community: https://discord.gg/anthropic

Highlighted Details

• Extensive examples for Retrieval Augmented Generation (RAG) with vector databases (Pinecone) and web data.
• Demonstrations of Claude's tool-use capabilities, including calculator integration and SQL query generation.
• Guides for multimodal interactions, covering image input processing and image generation via Stable Diffusion.
• Techniques for advanced use cases like sub-agent architectures, automated evaluations, and prompt caching.

Maintenance & Community

The project is maintained by Anthropic. Contributions are welcomed from the community via the issues page for new ideas or improvements. Links to official documentation and a Discord community are provided for support and discussion.

Licensing & Compatibility

The repository's license is not explicitly stated in the provided README. Code snippets are intended for integration into user projects, implying broad compatibility, but specific licensing for the code itself would need verification.

Limitations & Caveats

The cookbook focuses on Python examples, requiring adaptation for other programming languages. While it showcases various Claude features, users will need an Anthropic API key, which may involve costs depending on usage. The project is a collection of examples, not a standalone library, meaning direct integration requires understanding the underlying concepts and adapting the provided code.